Determining an invitational content item type based on predicted user attention

ABSTRACT

A media channel can include a mix of media items and invitational content items. At some point during the playback of the media channel an invitational content item can be presented. In some cases, the invitational content items eligible for presentation can be of differing types, such as video and audio. In can be advantageous to restrict presentation of video invitational content items to times when a user is likely to view the screen of the client device during playback of the invitational content item. To accomplish this one or more heuristics or rules can be applied to client device data to predict a user attention level. The user attention level can then be correlated to an invitational content item type, which can then be used to select an invitational content item for playback.

TECHNICAL FIELD

The present technology pertains to presenting invitational content, andmore specifically pertains to predicting user engagement in order toselect between audio and visual invitational content item types forpresentation in a media station on a client device.

BACKGROUND

Many users enjoy consuming content such as music or television showswithout having to purchase or maintain a copy of the media items.Traditionally, users accomplished this through radio or televisionbroadcasting. However, many users have turned to more flexible contentdistribution and consumption models offered through the Internet andportable electronic devices, such as media streaming services. Suchservices allow a user to stream an individual content sequence to anInternet connected device. While each individual user of the mediastreaming service is not required to purchase a copy of the media itemsconsumed, the media streaming service is generally required to pay a feeto the content providers. In order to fund a media streaming service anumber of new revenue models have been developed, many of which includepresenting invitational content, such as advertisements within the mediastream. The presentation of invitational content allows a mediastreaming service to offer the media items to a consumer at asignificantly reduced rate or even for free.

Traditionally, invitational content has been presented within a mediastream by inserting it between media items or by presenting it overtopof a media item. For example, an audio advertisement may be presented inbetween two songs, such that playback of the second song is preventeduntil the audio advertisement completes. In another example, a banneradvertisement may be presented across the top or bottom of a televisionshow. In a further example, a video advertisement may be presented sothat it takes over the full screen. The type of advertisement presented,e.g. audio, video, or banner, may be strictly dependent upon the type ofadvertisement provided by the advertiser. Such an arbitrary decisionmechanism can result in suboptimal results for all parties. For example,in some situations a user may be more receptive to one type ofadvertisement over another. Therefore, if an advertisement is presentedin the less ideal type, the user may be more likely to ignore theadvertisement. Alternatively, if one type of advertisement offers ahigher revenue stream for the media station provider, the media stationprovider may wish to try to present that advertisement type more often.However, such an approach could result in a decreased user experienceand dissatisfied advertisers who are unable to realize their advertisinggoals.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, and non-transitory computer-readablestorage media for presenting invitational content within a mediachannel. A media channel can be a mix of media items and invitationalcontent packages that can be played or executed by a media stationplayer on a client device. At some point during playback of the mediastation, an invitational content item can be presented. The set ofinvitational content items eligible for playback can includeinvitational content items of differing types, such as video and audioinvitational content items. In some cases, it can be advantageous topresent one type of invitational content item type over another. Forexample, if a user is not likely to look at the screen of the clientdevice during playback of the invitational content item, then it may bebetter to present an audio invitational content item.

To select an invitational content item, at least one attentionpredicting rule can be applied to client device data collected from theclient device currently executing the media station player to predict auser attention level. The client device data can include a variety ofdata points regarding the current state of the client device and/or themedia player application that are applicable to determining a user'slevel of engagement with the client device. The data items can includedevice type, accelerometer data, gyroscope data, compass data, mediaplayer state data, media stream state data, ambient light data, devicelock state, or device peripheral connection data. For example, a rulecan predict a user attention level of visual attentive when the clientdevice data includes screen unlocked and media player application openin the foreground. In another example, a rule can predict a userattention level of visual attentive when the client device data includesdevice type portable and device stationary. In a further example, a rulecan predict a user attention level of visual attentive when the clientdevice data includes connected to peripheral, device stationary, and atleast one of connected to a display or gyroscope data indicating devicepropped in a dock. In yet another example, a rule can predict a userattention level of visual attentive when the client device data includesmedia station type is soundtrack. In still another example, a rule canpredict a user attention level of audio attentive when the client devicedata include media station type is fitness or road trip. In anotherexample, a rule can predict a user attention level of visual attentivewhen the client device data includes at least one of media station entrydetected, new media station initiated, or media item skip detected.

Based on the predicted user attention level, an invitational contentitem type preference can be designated. The designation can be based ona correlation between a user attention level and an invitational contentitem type. For example, a user attention level of visual attentive canbe correlated with a video invitational content item type. In anotherexample, a user attention level of audio attentive can be correlatedwith an audio invitational content item type. The designatedinvitational content item type preference can be used as at least onefactor in selecting an invitational content item. In some cases, theinvitational content item type can be used in prioritizing a pluralityof invitational content items.

In some cases, a designated invitational content item type preferencecan be overridden after analyzing a second set of device data items. Forexample, a designated invitational content item type of video can beoverridden based on a media stream sponsor preference. In anotherexample, a designate invitational content item type of video can beoverridden based on an invitational content campaign preference. In afurther example, a designated invitational content item type of videocan be overridden when an ambient light data item is below a predefinedthreshold value. In still another example, a designated invitationalcontent item type of video can be overridden when a battery level isbelow a predefined threshold value.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates an exemplary media channel playback scenario;

FIG. 2 illustrates an exemplary user engagement predictor engine;

FIG. 3 illustrates a first exemplary attention predicting rule;

FIG. 4 illustrates a second exemplary attention predicting rule;

FIG. 5 illustrates a third exemplary attention predicting rule;

FIG. 6 illustrates an exemplary system configuration for presentinginvitational content within a media station;

FIG. 7 illustrates an exemplary method embodiment for selecting aninvitational content item for presentation within a media channel basedon predicted user attention level;

FIG. 8 illustrates an exemplary method embodiment for presenting aninvitational content item within a media channel based on predicted userattention level;

FIG. 9 illustrates an exemplary method embodiment for setting aninvitational content item type based on a predicted user attention leveland secondary client device data; and

FIG. 10A and FIG. 10B illustrate exemplary system embodiments.

DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.

The disclosed technology addresses the need in the art for a techniquefor determining what type of invitational content item, e.g. audio orvideo, should be presented within a media channel that can be consumedon a client device. A media channel or media station can be a sequenceof media items that can be played or executed by a media station playeron a client device. Some non-limiting examples of media items caninclude songs, podcasts, television shows, games, and/or videos. Othermedia items are also possible. The media station player can be anyapplication capable of media item playback, such as a component of awebpage, a plug-in, a client-side application, etc. In some cases, amedia station can be a continuous sequence of content such that as onecontent item completes playback a next item begins. The playback processof a continuous media stream can repeat until a user takes an action toterminate or temporarily delay the playback, such as quitting theapplication, switching to a different media station, pausing playback,or skipping a media item. However, a media station can also be definedto be a finite sequence of media items. A media station can behomogeneous or heterogeneous. That is, a media station can be designedto playback media items all of the same media type or of different mediatypes. For example, a homogeneous media station can playback only audiomedia items or only video media items. In another example, aheterogeneous media station can playback a mix of audio media items andvideo media items.

A media station can also be configured to play invitational content,such as advertisements, within the media stream. An invitational contentitem can include content found in a media item, such as a song or avideo, but an invitational content item can also include targetedcontent and/or content designed to elicit a response from a user.Therefore, an invitational content item and a media item can be distinctitem types, each of which can be presented in a media station. Theinvitational content can be used as a source of revenue and/or tosubsidize a media station so that the media items can be provided to endusers free of charge or for a reduced fee. In some cases, one type ofinvitational content can produce greater revenue than another. Forexample, an invitational content provider may be charged a higher ratefor video invitational content items.

The invitational content can be presented within a media station using avariety of techniques. In some cases, invitational content can bepresented to a user in a manner that prevents or blocks the playback ofa next media item or a next segment of a media item. For example, uponthe completion of the playback of a music item, but before beginningplayback of a new music item, an invitational content item can bepresented in the media stream. Invitational content can also bedisplayed in conjunction with a media item or media item representation.For example, an invitational content item can be presented in a bannerad displayed with a music album cover or during the playback of atelevision show.

Using the disclosed technology it is possible to increase the benefitsto invitational content providers, invitational content consumers, andthe media station service. An aspect of the disclosed technology thataids in the improvements involves analyzing device data to infer,deduce, or predict whether a user is likely to be actively engaged withthe client device or likely to pay attention to the client device,collectively referred to as user attention level. Based on the predicteduser attention level an invitational content item type can be selected.A person skilled in the relevant art will recognize that while thedisclosure frequently uses only two invitational content item types,i.e. audio and video, to illustrate the disclosed technology, thedisclosed technology can be used with additional invitational contentitem types, such as banner invitational content items.

FIG. 1 illustrates an exemplary media channel playback scenario 100. Themedia channel can include a mixture of media items and invitationalcontent items. At various points during playback of the media channel,an invitational content item can be presented, such as invitationalcontent item 102. The invitational content item can be presented inresponse to an invitational content triggering event, which can includelaunching a media station application; initiating a new media station;completing playback of a media item; activating a particular feature ofa user interface, e.g. a predefined screen in the user interface; movingthe device in a predefined manner, e.g. detecting movement using anaccelerometer; moving the device within a defined distance of apredefined sensor, e.g. a proximity sensor; and/or some other useraction, such as pausing for a predetermined period of time or skipping amedia item. Furthermore, the invitational content item can be presentedin a manner that prevents playback of a next media item, such as mediaitem 104, until all or a predetermined part of the invitational contentitem completes.

In response to detecting an invitational content item triggering event,one or more invitational content rules or heuristics can be applied todetermine whether an invitational content item should be presented, thetype of the invitational content item, and/or the actual invitationalcontent item. To determine the type of the invitational content item,the disclosed technology can include one or more attention predictingrules or heuristics. The attention predicting rules can be applied toclient device data to predict a user attention level.

FIG. 2 illustrates exemplary user engagement predictor engine 200. Userengagement predictor engine 200 can contain a number of components forpredicting a user attention level. The components can include one ormore databases for storing data relevant to the operation of the module,e.g. attention predicting rules database 220, and one or more modulesfor interacting with the databases and/or controlling the featuresprovided by user engagement predictor engine 200, e.g. device dataanalyzer 210. Each of the components in FIG. 2 is discussed in moredetail below; however, it should be understood by one skilled in theart, that the architectural configuration illustrated in FIG. 2 issimply one possible configuration and that other configurations withmore or less components is also possible.

User engagement predictor engine 200 can include one or more attentionpredicting rules for predicting a user attention level. A predicted userattention level can be selected from a set of predefined identifiers.The set of predefined identifiers can be defined with differing levelsof granularity based on the configuration of the system. For example,the set of predefined identifiers can be defined to express whether theuser is engaged with the client device in an audio or visual manner,e.g. visual attentive, audio attentive, and unknown. In another example,the set of predefined identifiers can be defined to express whether theuser is actively engaged with the client device or not, e.g. engaged,not engaged, and unclear. In a further example, the set of predefinedidentifiers can be defined to differentiate when the user is currentlyengaged with the client device in a visual manner and when the user maybe enticed to engage with the client device in a visual manner, e.g.visually engaged, in visual proximity, and audio engaged. Additional oralternative user attention level identifiers are also possible.Furthermore, the number and/or granularity of the identifiers in the setcan also vary.

Each user attention level identifier can be correlated with aninvitational content type. For example, the identifiers in the set ofvisual attentive, audio attentive, and unknown, can correlate to video,audio, and system default type, respectively. In another example, theidentifiers in the set of engaged, not engaged, and unclear, cancorrelate to video, audio, and system default, respectively. In afurther example, the set of visually engaged, in visual proximity, andaudio engaged, can correlate to video, video, and audio, respectively.

An invitational content type of system default can be defined to be adefault type that makes sense for the system, the media playerapplication, or even the media station, depending on the configurationof the system. For example, the system may have a policy of preferringaudio invitational content items when the user's level of visualengagement is not known. In another example, if the media station ishomogeneous and only includes music items, a default invitationalcontent item type may be audio. However, if the media station ishomogeneous and only includes visually based media items, such as videosor television shows, a default invitational content item type may bevideo. Additionally, instead of system default, the identifiers ofunknown or unclear can correlate to a specific type, such as audio.

Attention predicting rules database 220 can be populated with thevarious attention predicting rules. An attention predicting rule can beapplied to a client device data set when the media player application isactive such that based on the values in the client device data set auser attention level can be set. For example, an attention predictingrule can be based on the state of the device screen lock when the mediaplayer application is active. If the state is locked, the rule canpredict that the user attention level is audio attentive, while if thestate is unlocked, the rule can predict that the user attention level isvisual attentive.

In some cases, an attention predicting rule can be specific to one ormore media stations, one or more client device types, or some otherclient device or media station characteristic, such as whether theclient device is connected to a peripheral device. For example, someattention predicting rules may be specific to client devices that aregenerally stationary, such as desktop computing devices, smarttelevisions, or set-top boxes. In another example, some station sponsorsmay require a greater assurance that a user's visual attention is on theclient device, and thus some attention predicting rules may be specificto a particular media station. The attention predicting rules can beexpressed using a variety of formats and/or file types. For example, anattention predicting rule can be expressed using XML, or some othermark-up language. In another example, an attention predicting rule canbe expressed using computer executable instructions, such as javascript.

User engagement predictor engine 200 can also include device dataanalyzer 210. Device data analyzer 210 can be configured to predict auser attention level, such as visual attentive or audio attentive. Topredict a user attention level, device data analyzer 210 can apply oneor more attention predicting rules to client device data. The clientdevice data can be obtained from the client device on which theinvitational content triggering event occurred. Client device data caninclude a variety of data points regarding the current state of theclient device and/or the media player application, such as device type;accelerometer data; gyroscope data; compass data; media player statedata, e.g. open in the foreground; media stream state data, e.g. mediastation identifier, media item playing, user interactions, etc.; ambientlight data; device lock state; battery level data; and/or peripheralconnection data. Other client device data relevant to the current stateof the device and a user's level of engagement can also be collectedand/or used in predicting a user attention level.

User engagement predictor engine 200 can include a variety of attentionprediction rules or heuristics, which can be applied to client devicedata when the media player application is active. Each rule can examineone or more client device data items, such as device type or screen lockstate. FIG. 3 illustrates a first exemplary attention predicting rule300. In rule 300, user engagement predictor engine 200 can use twoclient device data items to predict a user attention level: screen lockstate and media player application state. First, user engagementpredictor engine 200 can examine the state of a screen lock data item tocheck if the screen on the client device is locked (302). If the screenis locked, user engagement predictor engine 200 can predict that theuser attention level is user not engaged (308). However, if the screenis not locked, user engagement predictor engine 200 can examine thestate of the media player application on the client device to check ifthe application is open in the foreground (304). If the media playerapplication is open in the foreground, user engagement predictor module200 can predict that the user attention level is user engaged (306).Alternatively, if the media player application is not open, userengagement predictor module 200 can predict that the user attentionlevel is user not engaged (308).

FIG. 4 illustrates a second exemplary attention predicting rule 400. Inrule 400, user engagement predictor engine 200 can again use two clientdevice data items to predict a user attention level: device type anddevice movement status. First, user engagement predictor engine 200 canexamine the device type to determine if the client device is of a typethat is generally stationary (402). For example, user engagementpredictor engine 200 can check if the client device type is of a desktoptype as opposed to a mobile device type. If the user engagementpredictor engine 200 determines the client device is generallystationary, user engagement predictor engine 200 can predict a userattention level of unknown (404). However, if user engagement predictorengine 200 determines the client device has a potential for regularmovement, user engagement predictor engine 200 can check if the clientdevice is currently moving (406). For example, data from anaccelerometer, gyroscope, and/or compass could indicate that the user ofthe client device is walking, running, biking, in a car, etc. If userengagement predictor engine 200 determines the client device is moving,user engagement predictor engine 200 can predict that the user attentionlevel is audio attentive (410). Otherwise, user engagement predictorengine 200 can predict that the user attention level is video attentive(408).

FIG. 5 illustrates a third exemplary attention predicting rule 500. Inrule 500, user engagement predictor engine 200 can use four clientdevice data items to predict a user attention level: peripheralconnection state, device movement status, peripheral type, and gyroscopedata. First, user engagement predictor engine 200 can examine theperipheral connection state to determine if the client device isconnected to a peripheral device (502). For example, user engagementpredictor engine 200 can check if the data indicates the client deviceis connected to a peripheral device via a Bluetooth connection, a cable,wireless, a dock, or AirPlay from Apple Inc. of Cupertino, Calif. Ifuser engagement predictor engine 200 determines the client device is notconnected to a peripheral, user engagement predictor engine 200 canpredict a user attention level of unknown (504). However, if userengagement predictor engine 200 determines the client device isconnected to a peripheral, user engagement predictor engine 200 can nextcheck if the device is moving (506). For example, data from anaccelerometer, gyroscope, and/or compass could indicate that the user ofthe client device is walking, running, biking, in a car, etc. If userengagement predictor engine 200 determines the client device is moving,user engagement predictor engine 200 can predict that the user attentionlevel is audio attentive (508). However, if user engagement predictorengine 200 determines the client device is not moving, user engagementpredictor engine 200 can check if the client device data indicates theclient device is connected to a video display (510). If user engagementpredictor engine 200 determines the client device is connected to avideo display, user engagement predictor engine 200 can predict a userattention level of visual attentive (512). Alternatively, if userengagement predictor engine 200 determines the client device is notconnect to a video display, user engagement predictor engine 200 cancheck if the gyroscope data indicates the client device is propped up ina dock (514). If user engagement predictor engine 200 determines theclient device is propped up in a dock, user engagement predictor enginecan predict a user attention level of visual attentive (512). Otherwise,user engagement predictor engine 200 can predict a user attention levelof audio attentive (516).

Additional attention predicting rules are also possible. For example, arule can be based on whether the client device data indicates a user isactively skipping tracks. If the user is actively skipping tracks, evenif using a remote, the user is engaged with the device. Since the useris actively engaged, the user may be receptive to a video invitationalcontent item type. Therefore, the rule can predict visual attentive inresponse to client device data that indicates the user is activelyskipping tracks. In another example, a rule can be based on whether themedia player application just started or started a new media station. Inthis situation the user is likely still looking at the application andmay be receptive to a video invitational content item. Therefore, therule can predict visual attentive in response to client device data thatindicates the media player application just started or started a newmedia station. In yet another example, a rule can examine motion data,such as data from an accelerometer, gyroscope, and/or compass todetermine whether a user is running. If the user is running, the user isnot likely to be looking at the screen and thus serving an audioinvitational content item may be more advantageous for the invitationalcontent provider. Accordingly, the rule can predict a user attentionlevel of audio attentive in response to data indicating the user isrunning. In still another example, a rule can be based on the mediastation content. That is, if a station is fitness oriented or road triporiented, the rule can predict a user attention level of audioattentive. Alternatively, if the station is soundtrack oriented, therule can predict a user attention level of video attentive. In a furtherexample, a rule can be specific to a sponsor of a curated media station.That is, the sponsor may pay a different rate in order to guarantee thata particular type of invitational content is presented at specificpoints during the playback or even at any point during playback. Inanother example, a rule can be based on the orientation of the device.If gyroscope data indicates that the client device is propped up, suchas in a dock or using a smart cover, the rule can predict a userattention level of video attentive. Additionally, rules are alsopossible, such as rules based strictly on device type.

In some cases, a rule can be based on client device data that caninfluence a user experience but which does not necessarily indicatewhether a user is likely to be engaged with a client device, such asambient light data or battery level data. For example, a rule canpredict a user attention level of audio attentive when the ambient lightdata indicates the client device is being used in a dark room. This rulecan be based on an assumption that playing a video in dark room isdisruptive due to the amount of light a video will give off. In anotherexample, a rule can predict a user attentive level of audio when theclient device battery level is below a predefined threshold value. Thisrule can be based on a user experience policy of not wanting to strainthe battery once it has reached a certain minimum level.

In some cases, device data analyzer 210 can apply all of the attentionpredicting rules and combine the results to generate a single predicteduser attention level. For example, several predicted user attentionlevels could be combined by selecting the user attention level valuethat occurs most frequently. Alternatively, a user attention level ofvideo attentive can be predicted if any of the rules predict videoattentive. Additional techniques for combining multiple predicted userattention levels are also possible.

Device data analyzer 210 can also be configured to apply a subset of theattention predicting rules or even just a single rule. For example, ifone or more of the attention predicting rules does not apply to theclient device or the current media station, device data analyzer 210 maynot apply those rules. In another example, device data analyzer 210 mayapply a single attention predicting rule based on a best fitting rule,such as device type.

Each client device data item value can be assigned a weight to give it agreater or lesser impact on the predicted user attention level. Theweight can be based on a variety of factors, such as the significance ofthe value and/or the confidence in the accuracy of the value. In someconfigurations, weights can be used to indicate that the predicted userattention level can be used even if the attention prediction rule wasnot fully satisfied, so long as the client device data items with thehigher weights are satisfied.

Each attention predicting rule or heuristic can also be assigned aweight to give it a greater or lesser impact on the predicted userattention level. For example, if more than one attention predicting rulecan be applied to the client device data, device data analyzer 210 couldchoose the rule with the greatest weight or the top n rules. In somecases, the weight could reflect a degree of accuracy in the prediction.Such a weight could be used to exclude those rules with a weight below apredefined threshold value. For example, an invitational contentprovider could indicate that their video invitational content item canbe presented only in conjunction with user attention levels predictedusing rules with a weight above a specified threshold value.

In some configurations, device data analyzer 210 can assign a confidencescore to the predicted user attention level. The confidence score canindicate a likelihood that the predicted value is correct. For example,a predicted user attention level can be assigned a confidence score inthe range [0,1], where 0 indicates no confidence and 1 indicates fullconfidence. In another example, a confidence score can be expressed as apercentage, such as 75%. Other relative scales can also be used.

In some cases, confidence scores can be used aid in determining whichpredicted user attention level should be used. For example, if attentionpredicting rule 1 predicted a user attention level of audio attentive,while attention predicting rule 2 predicted visual attentive, theconflict can be resolved by using the predicted value with the highestconfidence score.

The calculation of a confidence score can depend on a variety offactors. In some configurations, how the client device data items areobtained, the weight assigned to a client device data item, or thenumber of data items considered in predicting the user attention levelcan be a factor in the confidence score. The confidence score can alsobe related to the strength of the attention predicting rule used inpredicting the user attention level. In some cases, user engagementpredictor engine 200 can be configured with a minimum user attentionlevel confidence score. In cases where a confidence score for apredicted user attention level is not high enough, the predicted userattention level can be disregarded.

An exemplary system configuration 600 for presenting invitationalcontent within a media station is illustrated in FIG. 6 whereinelectronic devices communicate via a network for purposes of exchangingcontent and other data. The system can be configured for use on a widearea network, such as that illustrated in FIG. 6. However, the presentprinciples are applicable to a wide variety of network configurationsthat facilitate the intercommunication of electronic devices. Forexample, each of the components of system 600 in FIG. 6 can beimplemented in a localized or distributed fashion in a network.

In system 600, media items and invitational content items can bedelivered to client devices 602 ₁, 602 ₂, . . . , 602 _(n) (collectively“602”) connected to network 604 by direct and/or indirect communicationswith media content server 606 and/or invitational content server 608.Client devices 602 can be any network enabled computing devices capableof receiving content for playback within a media channel such as desktopcomputers; mobile computers; handheld communications devices, e.g.mobile phones, smart phones, tablets; smart televisions; set-top boxes;and/or any other network-enabled computing devices. Furthermore, mediacontent server 606 and/or invitational content server 608 canconcurrently accept connections from and interact with multiple clientdevices 602.

Although media content server 606 and invitational content server 608are presented herein as separate entities, this is for illustrativepurposes only. In some configurations, media content server 606 andinvitational content server 608 can be the same entity. Thus, a singleentity can provide the media items and the invitational content items.Additionally, system 600 can be configured with multiple media contentservers and/or invitational content servers. For example, system 600 caninclude invitational content servers for different types of contentitems, e.g. audio, video, banner, etc. Furthermore, in someconfigurations, a content distribution network can act as anintermediary between client devices 602 and one or more content servers.

In some configurations, a client device can request an invitationalcontent item from a content server at the time that a content item isneeded for playback. In this configuration, user engagement predictorengine 200 can be installed on either the content server or the clientdevice. For example, user engagement predictor engine 200 can beinstalled on the invitational content server and the client device cansend client device data along with the request for an invitationalcontent item. User engagement predictor engine 200 can apply one or moreattention predicting rules to client device data to predict a userattention level and designate an invitational content item type. Theinvitational content server can select an invitational content itembased at least in part on the designated invitational content item type.Alternatively, user engagement predictor engine 200 can be installed onthe client device, such as part of the media player application. In thiscase, the client can send the predicted user attention level and/or thedesignated invitational content type to the content server with therequest. The invitational content server can then select an invitationalcontent item based at least in part on the received predicted userattention level and/or the designated invitational content type. In somecases, user engagement predictor engine 200 can be installed on thecontent server regardless of whether it is also installed on clientdevices. Additionally, whether user engagement predictor engine 200resides on a client device can be dependent on the type of the clientdevice. For example, user engagement predictor engine 200 may only beinstalled on client devices with certain processing capabilities.

A system configured such that a client device must request aninvitational content item from a content server at the time a contentitem is needed for playback can create a heavy burden on the clientdevice and can have performance issues when used in situations with lessreliable or inconsistent Internet connections. To mitigate thesedisadvantages, one or more content items and one or more rules can becached on the client device. The caching makes it possible to shift tothe client device at least a portion of the decision-making regardingwhich content to present and when. In this configuration, a clientdevice can include user engagement predictor engine 200. When needed,the client device can apply one or more attention predicting rules toclient device data to predict user attention level and determine aninvitational content type. In particular, the present technology cancache a list of one or more invitational content items, each assigned aninvitational content item type, and when needed, the client device canpredict a user attention level to select an invitational content itemwith an appropriate type.

FIG. 7 is a flowchart illustrating steps in exemplary method 700 forselecting an invitational content item for presentation within a mediachannel based on predicted user attention level. For the sake ofclarity, this method is discussed in terms of exemplary user engagementpredictor engine 200 in FIG. 2 and exemplary invitational content server608 in FIG. 6. Although specific steps are shown in FIG. 7, in otherembodiments a method can have more or less steps.

At some point during the playback of a media channel, invitationalcontent server 608 configured with user engagement predictor engine 200can receive a request from client device 602 _(i) for an invitationalcontent item (702). The request can include a set of client device dataitems. Alternatively, invitational content item server 608 can requestthe client device data items from requesting client device 602 _(i).

After receiving the request, invitational content server 608 can checkif there are eligible invitational content items for client device 602_(i) with differing invitational content item types (704). For example,invitational content server 608 can determine whether the user iseligible to receive invitational content items of type audio and video.If so, invitational content server 608 can apply one or more attentionpredicting rules to the received client device data to predict a userattention level (706). The attention predicting rules can be any of thepreviously discussed rules, such as those in FIGS. 3-5, or any otherattention predicting rule. In some cases, invitational content server608 can assign a confidence score to the predicted user attention level.

After predicting a user attention level, invitational content server 608can check if the user attention level is visual attentive (708). If so,invitational content server 608 can set an invitational content itemtype as video (710). Alternatively, invitational content server 608 canset an invitational content item type of banner or visual, which can beused to serve video or other visual invitational content items types. Ifthe user attention level is not visual, invitational content server 608can set the invitational content item type as audio (712).

After setting an invitational content item type or determining that onlyone type is currently available to deliver to client device 602 _(i),invitational content server 608 can select an invitational content item(714). The selection can be based on invitational content item type, aswell as other factors, such as user characteristics or demographic data.Additionally, the invitational content item type can be used toprioritize invitational content items. For example, the designatedinvitational content item type can be a factor in a prioritizationalgorithm. The algorithm can be configured to consider a variety ofother factors, such as user characteristics, to identify top candidateinvitational content items. Therefore, when using a prioritizationalgorithm, an invitational content item having a type different than thedesignated type could be selected if other factors indicate theinvitational content item would be a better fit. After sending aninvitational content item, invitational content server 608 can resumeprevious processing, which can include repeating method 700.

FIG. 8 is a flowchart illustrating steps in exemplary method 800 forpresenting an invitational content item within a media channel based onpredicted user attention level. For the sake of clarity, this method isdiscussed in terms of exemplary user engagement predictor engine 200 inFIG. 2 and client device 602 _(i) in FIG. 6. Although specific steps areshown in FIG. 8, in other embodiments a method can have more or lesssteps.

At some point during the playback of a media channel, client device 602_(i) configured with user engagement predictor engine 200 can detect aninvitational content triggering event (802). An invitational contenttriggering event can include launching a media station application;initiating a new media station; completing playback of a media item;activating a particular feature of a user interface, e.g. a predefinedscreen in the user interface; moving the device in a predefined manner,e.g. detecting movement using an accelerometer; moving the device withina defined distance of a predefined sensor, e.g. a proximity sensor;and/or some other user action, such as pausing for a predeterminedperiod of time or skipping a media item.

In response to detecting the triggering event, client device 602 _(i)can collect a variety of client device data (804) relevant to thecurrent state of client device 602 _(i). Client device data can includea variety of data points regarding the current state of the clientdevice and/or the media player application, such as device type;accelerometer data; gyroscope data; compass data; media player statedata, e.g. open in the foreground; media stream state data, e.g. mediastation identifier, media item playing, user interactions, etc.; ambientlight data; device lock state; battery level data; and/or peripheralconnection data. Other client device data relevant to the current stateof the device can also be collected.

Client device 602 _(i) can apply at least one attention predicting ruleto the collected client device data to predict a user attention level(806). The attention predicting rules can be any of the previouslydiscussed rules, such as those in FIGS. 3-5, or any other attentionpredicting rule. After predicting a user attention level, client device602 _(i) can set an invitational content item type (808) based on thepredicted user attention level. For example, if the user attention levelis visual attentive, client device 602 _(i) can set the invitationalcontent item type as video. In another example, if the predicted userattention level is visually engaged or in visual proximity, clientdevice 602 _(i) can set the invitational content item type as video. Inyet another example, if the predicted user attention level is unclear orunknown, client device 602 _(i) can set the invitational content itemtype to a default value, such as audio or banner. In a further example,if the predicted user attention level is audio attentive or not engaged,client device 602 _(i) can set the invitational content item type asaudio.

After designating an invitational content item type, client device 602_(i) can obtain an invitational content item based at least in part onthe invitational content item type (810). In some cases, client device602 _(i) can obtain the invitational content item by requesting an itemfrom an invitational content server. As part of the request clientdevice 602 _(i) can provide the designated invitational content itemtype. Client device 602 _(i) can also be configured to cacheinvitational content items. In this case, client device 602 _(i) canselect an invitational content item from the cache using theinvitational content item type as a selection factor.

After obtaining an invitational content item, client device 602 _(i) canpresent the invitational content item in the media channel (812). Afterpresenting the invitational content item, client device 602 _(i) canresume previous processing, which can include repeating method 800.

FIG. 9 is a flowchart illustrating steps in exemplary method 900 forsetting an invitational content item type based on a predicted userattention level and secondary client device data. For the sake ofclarity, this method is discussed in terms of exemplary user engagementpredictor engine 200 in FIG. 2 and invitational content server 608 inFIG. 6. Although specific steps are shown in FIG. 9, in otherembodiments a method can have more or less steps.

At some point invitational content server 608 configured with userengagement predictor engine 200 can apply a first set of attentionpredicting rules to a first set of client device data items to predict auser attention level (902). The attention predicting rules can be any ofthe previously discussed rules, such as those in FIGS. 3-5, or any otherattention predicting rule. After predicting a user attention level,invitational content server 608 can set an invitational content itemtype based on the predicted user attention level (904). For example, ifthe predicted user attention level is visual attentive, invitationalcontent server 608 can set an invitational content type of video. Inanother example, if the predicted user attention level is audioattentive, invitational content server 608 can set an invitationalcontent type of audio.

Invitational content server 608 can also analyze a second set of clientdevice data items (906). The second set of client device data items canbe data that may influence a user experience but which does notnecessarily indicate whether a user is likely to be engaged with aclient device, such as ambient light data or battery level data. Basedon the analysis of the second set of client device data items,invitational content server 608 can set a user attention level overridevalue (908). For example, if invitational content server 608 determinesfrom the ambient light data that the light in the room is below apredefined threshold value, invitational content server 608 can set theuser attention level override value as override video. In anotherexample, if invitational content server 608 determines from the batterylevel data that the battery level is below a predefined threshold, e.g.20 percent, invitational content server 608 can set the user attentionlevel override value as override video. An analysis of the second set ofclient device data items can also result in setting the user attentionlevel override value as override audio.

After setting the user attention level override value, invitationalcontent server 608 can check if the invitational content type should beoverridden (910). If the invitational content type should be overridden,invitational content server 608 can re-assign the invitational contenttype (912). For example, if the invitational content item type is videoand the user attention level override value is override video,invitational content server 608 can set the invitational content type asaudio. In another example, if the invitational content item type isvideo and the user attention level override value is override video,invitational content server 608 can set the invitational content type asbanner. An audio invitational content type can also be overridden to bevideo, such as when media station data indicates a video should beplayed, a media station sponsor has expressed a preference of video, oran invitational content campaign preference.

If the invitational content type should not be overridden or afterre-assigning the invitational content type, invitational content server608 can select an invitational content item based at least in part onthe invitational content item type (914). After selecting theinvitational content item, invitational content server 608 can resumeprevious processing, which can include repeating method 900.

FIG. 10A, and FIG. 10B illustrate exemplary possible system embodiments.The more appropriate embodiment will be apparent to those of ordinaryskill in the art when practicing the present technology. Persons ofordinary skill in the art will also readily appreciate that other systemembodiments are possible.

FIG. 10A illustrates a conventional system bus computing systemarchitecture 1000 wherein the components of the system are in electricalcommunication with each other using a bus 1005. Exemplary system 1000includes a processing unit (CPU or processor) 1010 and a system bus 1005that couples various system components including the system memory 1015,such as read only memory (ROM) 1020 and random access memory (RAM) 1025,to the processor 1010. The system 1000 can include a cache of high-speedmemory connected directly with, in close proximity to, or integrated aspart of the processor 1010. The system 1000 can copy data from thememory 1015 and/or the storage device 1030 to the cache 1012 for quickaccess by the processor 1010. In this way, the cache can provide aperformance boost that avoids processor 1010 delays while waiting fordata. These and other modules can control or be configured to controlthe processor 1010 to perform various actions. Other system memory 1015may be available for use as well. The memory 1015 can include multipledifferent types of memory with different performance characteristics.The processor 1010 can include any general purpose processor and ahardware module or software module, such as module 1 1032, module 21034, and module 3 1036 stored in storage device 1030, configured tocontrol the processor 1010 as well as a special-purpose processor wheresoftware instructions are incorporated into the actual processor design.The processor 1010 may essentially be a completely self-containedcomputing system, containing multiple cores or processors, a bus, memorycontroller, cache, etc. A multi-core processor may be symmetric orasymmetric.

To enable user interaction with the computing device 1000, an inputdevice 1045 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 1035 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems can enable a user to provide multiple types of input tocommunicate with the computing device 1000. The communications interface1040 can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 1030 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 1025, read only memory (ROM) 1020, andhybrids thereof.

The storage device 1030 can include software modules 1032, 1034, 1036for controlling the processor 1010. Other hardware or software modulesare contemplated. The storage device 1030 can be connected to the systembus 1005. In one aspect, a hardware module that performs a particularfunction can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 1010, bus 1005, display 1035, and soforth, to carry out the function.

FIG. 10B illustrates a computer system 1050 having a chipsetarchitecture that can be used in executing the described method andgenerating and displaying a graphical user interface (GUI). Computersystem 1050 is an example of computer hardware, software, and firmwarethat can be used to implement the disclosed technology. System 1050 caninclude a processor 1055, representative of any number of physicallyand/or logically distinct resources capable of executing software,firmware, and hardware configured to perform identified computations.Processor 1055 can communicate with a chipset 1060 that can controlinput to and output from processor 1055. In this example, chipset 1060outputs information to output 1065, such as a display, and can read andwrite information to storage device 1070, which can include magneticmedia, and solid state media, for example. Chipset 1060 can also readdata from and write data to RAM 1075. A bridge 1080 for interfacing witha variety of user interface components 1085 can be provided forinterfacing with chipset 1060. Such user interface components 1085 caninclude a keyboard, a microphone, touch detection and processingcircuitry, a pointing device, such as a mouse, and so on. In general,inputs to system 1050 can come from any of a variety of sources, machinegenerated and/or human generated.

Chipset 1060 can also interface with one or more communicationinterfaces 1090 that can have different physical interfaces. Suchcommunication interfaces can include interfaces for wired and wirelesslocal area networks, for broadband wireless networks, as well aspersonal area networks. Some applications of the methods for generating,displaying, and using the GUI disclosed herein can include receivingordered datasets over the physical interface or be generated by themachine itself by processor 1055 analyzing data stored in storage 1070or 1075. Further, the machine can receive inputs from a user via userinterface components 1085 and execute appropriate functions, such asbrowsing functions by interpreting these inputs using processor 1055.

It can be appreciated that exemplary systems 1000 and 1050 can have morethan one processor 1010 or be part of a group or cluster of computingdevices networked together to provide greater processing capability.

For clarity of explanation, in some instances the present technology maybe presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, and so on. Functionality described herein also can beembodied in peripherals or add-in cards. Such functionality can also beimplemented on a circuit board among different chips or differentprocesses executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

1. A computer-implemented method comprising: determining an existence ofa plurality of invitational content item types including at least audioand video; predicting a user attention level by applying at least oneattention predicting rule to client device data; designating aninvitational content item type preference based on the predicted userattention level; and selecting an invitational content item based atleast in part on the invitational content item type preference.
 2. Themethod of claim 1, wherein predicting a user attention level furthercomprises: predicting a user attention level of visual attentive whenthe client device data includes screen unlocked and media playerapplication open in foreground.
 3. The method of claim 1, whereinpredicting a user attention level further comprises: predicting a userattention level of visual attentive when the client device data includesdevice type portable and device stationary.
 4. The method of claim 1,wherein predicting a user attention level further comprises: predictinga user attention level of visual attentive when the client device dataincludes connected to peripheral, device stationary, and at least one ofconnected to video display or gyroscope data indicates device propped indock.
 5. The method of claim 1, wherein predicting a user attentionlevel further comprises: predicting a user attention level of visualattentive when the client device data includes media station type issoundtrack.
 6. The method of claim 1, wherein predicting a userattention level further comprises: predicting a user attention level ofaudio attentive when the client device data includes media station typeis fitness or road trip.
 7. The method of claim 1, wherein predicting auser attention level further comprises: predicting a user attentionlevel of visual attentive when the client device data includes at leastone of media station entry detected, new media station initiated, ormedia item skip detected.
 8. A computer-implemented method comprising:predicting, via a processor, a user attention level by applying one ormore user attention predicting rules to client device data; designatingan invitational content item type based on the predicted user attentionlevel; and presenting an invitational content item matching thedesignated invitational content type within a media stream.
 9. Themethod of claim 8, wherein predicting a user attention level occurs inresponse to detecting an invitational content item triggering eventduring playback of the media stream.
 10. The method of claim 8, whereinthe invitational content item type is selected from a set consisting ofan audio type and a video type.
 11. The method of claim 8, whereinclient device state data includes at least one of device type,accelerometer data, gyroscope data, compass data, media player statedata, media stream state data, ambient light data, device lock state, ordevice peripheral connection data.
 12. The method of claim 8, wherein avideo invitational content item is selected when user attention level isidentified as visually attentive.
 13. A manufacture comprising: anon-transitory computer-readable storage medium; and a computerexecutable instruction stored on the non-transitory computer-readablestorage medium which, when executed by a computing device, causes thecomputing device to perform a method comprising: applying one or moreuser attention heuristics to a set of device data items to predict auser attention level, the user attention level indicating a likelihoodthat a user will view an invitational content item presented on adevice; assigning an invitational content item type of video when thepredicted user attention level is visual attentive; and selecting aninvitational content item based on the invitational content item type,the invitational content item selected for presentation within a mediastream of content items and invitational content items.
 14. Themanufacture of claim 13, wherein each user attention heuristic in theone or more user attention heuristics is assigned a weight, andoverriding the invitational content item type of video when all devicedata items are below a predefined threshold weight.
 15. The manufactureof claim 13, further comprising: overriding the assigned invitationalcontent item type of video based on an analysis of a second set ofdevice data items.
 16. The manufacture of claim 13, further comprising:overriding the assigned invitational content item type of video based ona media stream sponsor preference.
 17. The manufacture of claim 13,further comprising: overriding the assigned invitational content itemtype of video based on invitational content campaign preferences. 18.The manufacture of claim 13, further comprising: assigning a confidencescore to the predicted user attention level.
 19. The manufacture ofclaim 13, wherein selecting an invitational content item furthercomprises: prioritizing a plurality of invitational content items basedon the invitational content item type.
 20. A system comprising: aprocessor; a first module configured to control the processor to detectan invitational content item triggering event during playback of a mediastream; a second module configured to control the processor to predict auser attention level by applying at least one attention predicting ruleto client device data; a third module configured to control theprocessor to set an invitational content item type based on thepredicted user attention level, wherein the predicted user attentionlevel is one of visual attentive or audio attentive; and a fourth moduleconfigured to control the processor to select an invitational contentitem based on the invitational content item type.
 21. The system ofclaim 20, wherein the invitational content item type is selected from aset comprising an audio type and a video type.
 22. The system of claim21, wherein an invitational content item with a type of audio isassigned a lower cost than an invitational content item with a type ofvideo.
 23. The system of claim 21, wherein selecting an invitationalcontent item further comprises: selecting an invitational content itemhaving an audio type when the predicted user attention level is audioattentive; and selecting an invitational content item having a videotype when the predicted attention level is visually attentive.
 24. Acomputer-implemented method comprising: applying, via a processor, afirst set of attention predicting rules to a first set of device dataitems to predict a user attention level; designating an invitationalcontent item type based on the predicted user attention level; analyzinga second set of device data items to generate a user attention leveloverride value; and selecting an invitational content item forpresentation within a media stream, the selecting based at least in parton the invitational content item type and the user attention leveloverride value.
 25. The method of claim 24, wherein the first set ofdevice state data items includes at least one of device type,accelerometer data, gyroscope data, compass data, media player statedata, media stream state data, device lock state, or device peripheralconnection data, and the second set of device state data items includesat least one of battery level or ambient light.
 26. The method of claim24, wherein the predicted user attention is visual attentive, the secondset of device state data includes a battery level below a predefinedthreshold, the user attention level override value is override video,and the selected invitational content item type is audio.
 27. The methodof claim 24, wherein the predicted user attention is visual attentive,the second set of device state data includes ambient light data below apredefined threshold, the user attention level override value isoverride video, and the selected invitational content item type isaudio.
 28. The method of claim 24, wherein selecting an invitationalcontent item further comprises prioritizing a plurality of invitationalcontent items based on the invitational content item type.